Overview

Dataset statistics

Number of variables12
Number of observations253
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.8 KiB
Average record size in memory96.5 B

Variable types

Numeric12

Warnings

target_home_price is highly correlated with population and 3 other fieldsHigh correlation
population is highly correlated with target_home_price and 5 other fieldsHigh correlation
monthly_supply_houses is highly correlated with new_permits_thousands and 2 other fieldsHigh correlation
new_permits_thousands is highly correlated with monthly_supply_houses and 5 other fieldsHigh correlation
mortgage_rate is highly correlated with target_home_price and 5 other fieldsHigh correlation
gdp_monthly is highly correlated with employment_percentageHigh correlation
hcai is highly correlated with population and 5 other fieldsHigh correlation
employment_percentage is highly correlated with population and 7 other fieldsHigh correlation
ppi_const_goods is highly correlated with target_home_price and 6 other fieldsHigh correlation
cci_real_estate is highly correlated with monthly_supply_houses and 3 other fieldsHigh correlation
deliquency_rate is highly correlated with monthly_supply_houses and 3 other fieldsHigh correlation
m3_trillion is highly correlated with target_home_price and 4 other fieldsHigh correlation
target_home_price is highly correlated with population and 4 other fieldsHigh correlation
population is highly correlated with target_home_price and 5 other fieldsHigh correlation
monthly_supply_houses is highly correlated with new_permits_thousandsHigh correlation
new_permits_thousands is highly correlated with monthly_supply_houses and 4 other fieldsHigh correlation
mortgage_rate is highly correlated with target_home_price and 5 other fieldsHigh correlation
hcai is highly correlated with population and 5 other fieldsHigh correlation
employment_percentage is highly correlated with population and 6 other fieldsHigh correlation
ppi_const_goods is highly correlated with target_home_price and 5 other fieldsHigh correlation
cci_real_estate is highly correlated with new_permits_thousands and 2 other fieldsHigh correlation
deliquency_rate is highly correlated with target_home_price and 2 other fieldsHigh correlation
m3_trillion is highly correlated with target_home_price and 5 other fieldsHigh correlation
target_home_price is highly correlated with population and 2 other fieldsHigh correlation
population is highly correlated with target_home_price and 4 other fieldsHigh correlation
new_permits_thousands is highly correlated with employment_percentageHigh correlation
mortgage_rate is highly correlated with population and 3 other fieldsHigh correlation
hcai is highly correlated with population and 3 other fieldsHigh correlation
employment_percentage is highly correlated with new_permits_thousandsHigh correlation
ppi_const_goods is highly correlated with target_home_price and 4 other fieldsHigh correlation
cci_real_estate is highly correlated with deliquency_rateHigh correlation
deliquency_rate is highly correlated with cci_real_estateHigh correlation
m3_trillion is highly correlated with target_home_price and 4 other fieldsHigh correlation
new_permits_thousands is highly correlated with hcai and 10 other fieldsHigh correlation
hcai is highly correlated with new_permits_thousands and 10 other fieldsHigh correlation
target_home_price is highly correlated with new_permits_thousands and 10 other fieldsHigh correlation
mortgage_rate is highly correlated with new_permits_thousands and 10 other fieldsHigh correlation
gdp_monthly is highly correlated with new_permits_thousands and 8 other fieldsHigh correlation
cci_real_estate is highly correlated with new_permits_thousands and 10 other fieldsHigh correlation
ppi_const_goods is highly correlated with new_permits_thousands and 10 other fieldsHigh correlation
monthly_supply_houses is highly correlated with new_permits_thousands and 8 other fieldsHigh correlation
employment_percentage is highly correlated with new_permits_thousands and 9 other fieldsHigh correlation
deliquency_rate is highly correlated with new_permits_thousands and 9 other fieldsHigh correlation
population is highly correlated with new_permits_thousands and 10 other fieldsHigh correlation
m3_trillion is highly correlated with new_permits_thousands and 10 other fieldsHigh correlation
target_home_price has unique values Unique
population has unique values Unique
m3_trillion has unique values Unique

Reproduction

Analysis started2021-09-09 12:56:42.219893
Analysis finished2021-09-09 12:57:08.556253
Duration26.34 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

target_home_price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct253
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean161.9776917
Minimum100.552
Maximum238.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2021-09-09T18:27:08.822239image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum100.552
5-th percentile110.239
Q1140.358
median161.991
Q3183.068
95-th percentile213.7768
Maximum238.53
Range137.978
Interquartile range (IQR)42.71

Descriptive statistics

Standard deviation30.99211294
Coefficient of variation (CV)0.1913356871
Kurtosis-0.5554807135
Mean161.9776917
Median Absolute Deviation (MAD)21.21
Skewness0.1097233212
Sum40980.356
Variance960.5110645
MonotonicityNot monotonic
2021-09-09T18:27:08.979328image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
118.6871
 
0.4%
138.6691
 
0.4%
148.0881
 
0.4%
229.4421
 
0.4%
206.1991
 
0.4%
136.6741
 
0.4%
180.8951
 
0.4%
195.8811
 
0.4%
174.4421
 
0.4%
213.371
 
0.4%
Other values (243)243
96.0%
ValueCountFrequency (%)
100.5521
0.4%
101.3391
0.4%
102.1271
0.4%
102.9221
0.4%
103.6771
0.4%
104.4241
0.4%
105.0541
0.4%
105.7681
0.4%
106.5371
0.4%
107.3811
0.4%
ValueCountFrequency (%)
238.531
0.4%
235.5831
0.4%
232.6561
0.4%
229.4421
0.4%
225.9541
0.4%
222.7351
0.4%
219.8991
0.4%
218.3561
0.4%
217.811
0.4%
217.5541
0.4%

population
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct253
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean308360.8854
Minimum281083
Maximum330968
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2021-09-09T18:27:09.136035image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum281083
5-th percentile284083.6
Q1295359
median309847
Q3321641
95-th percentile329357.6
Maximum330968
Range49885
Interquartile range (IQR)26282

Descriptive statistics

Standard deviation14854.48623
Coefficient of variation (CV)0.04817240752
Kurtosis-1.217562997
Mean308360.8854
Median Absolute Deviation (MAD)12990
Skewness-0.1880530206
Sum78015304
Variance220655761.3
MonotonicityStrictly increasing
2021-09-09T18:27:09.324377image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2841661
 
0.4%
3142721
 
0.4%
2865331
 
0.4%
2896061
 
0.4%
3270051
 
0.4%
3182841
 
0.4%
3147031
 
0.4%
3249441
 
0.4%
2875711
 
0.4%
3240521
 
0.4%
Other values (243)243
96.0%
ValueCountFrequency (%)
2810831
0.4%
2812991
0.4%
2815311
0.4%
2817631
0.4%
2819961
0.4%
2822471
0.4%
2825041
0.4%
2827691
0.4%
2830331
0.4%
2832851
0.4%
ValueCountFrequency (%)
3309681
0.4%
3309241
0.4%
3308291
0.4%
3306921
0.4%
3305351
0.4%
3303681
0.4%
3301991
0.4%
3300381
0.4%
3298941
0.4%
3297601
0.4%

monthly_supply_houses
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct63
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.759288538
Minimum3.5
Maximum12.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2021-09-09T18:27:09.512741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum3.5
5-th percentile3.8
Q14.3
median5.3
Q36.6
95-th percentile10.38
Maximum12.2
Range8.7
Interquartile range (IQR)2.3

Descriptive statistics

Standard deviation1.917019297
Coefficient of variation (CV)0.3328569639
Kurtosis1.155662563
Mean5.759288538
Median Absolute Deviation (MAD)1.1
Skewness1.296603831
Sum1457.1
Variance3.674962984
MonotonicityNot monotonic
2021-09-09T18:27:09.686275image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
414
 
5.5%
5.313
 
5.1%
5.512
 
4.7%
4.111
 
4.3%
4.311
 
4.3%
4.29
 
3.6%
5.29
 
3.6%
3.88
 
3.2%
4.58
 
3.2%
4.97
 
2.8%
Other values (53)151
59.7%
ValueCountFrequency (%)
3.55
 
2.0%
3.65
 
2.0%
3.72
 
0.8%
3.88
3.2%
3.95
 
2.0%
414
5.5%
4.111
4.3%
4.29
3.6%
4.311
4.3%
4.47
2.8%
ValueCountFrequency (%)
12.21
 
0.4%
11.61
 
0.4%
11.41
 
0.4%
11.31
 
0.4%
11.21
 
0.4%
111
 
0.4%
10.91
 
0.4%
10.73
1.2%
10.53
1.2%
10.31
 
0.4%

new_permits_thousands
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct232
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1324.134387
Minimum513
Maximum2263
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2021-09-09T18:27:09.827341image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum513
5-th percentile579.6
Q11003
median1302
Q31665
95-th percentile2115.6
Maximum2263
Range1750
Interquartile range (IQR)662

Descriptive statistics

Standard deviation470.4093853
Coefficient of variation (CV)0.3552580386
Kurtosis-0.8987729005
Mean1324.134387
Median Absolute Deviation (MAD)349
Skewness0.0551663
Sum335006
Variance221284.9898
MonotonicityNot monotonic
2021-09-09T18:27:09.984428image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10943
 
1.2%
15982
 
0.8%
16652
 
0.8%
5582
 
0.8%
16262
 
0.8%
5832
 
0.8%
15702
 
0.8%
18032
 
0.8%
14072
 
0.8%
15092
 
0.8%
Other values (222)232
91.7%
ValueCountFrequency (%)
5131
0.4%
5211
0.4%
5421
0.4%
5451
0.4%
5541
0.4%
5561
0.4%
5582
0.8%
5601
0.4%
5631
0.4%
5751
0.4%
ValueCountFrequency (%)
22631
0.4%
22191
0.4%
22181
0.4%
22121
0.4%
22031
0.4%
21781
0.4%
21701
0.4%
21502
0.8%
21411
0.4%
21391
0.4%

mortgage_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct185
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.241976285
Minimum3.31
Maximum8.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2021-09-09T18:27:10.125492image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum3.31
5-th percentile3.542
Q14.1
median5.03
Q36.22
95-th percentile7.336
Maximum8.62
Range5.31
Interquartile range (IQR)2.12

Descriptive statistics

Standard deviation1.277572431
Coefficient of variation (CV)0.243719613
Kurtosis-0.7238293722
Mean5.241976285
Median Absolute Deviation (MAD)1.07
Skewness0.4296252935
Sum1326.22
Variance1.632191317
MonotonicityNot monotonic
2021-09-09T18:27:10.282572image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.924
 
1.6%
4.324
 
1.6%
3.884
 
1.6%
6.223
 
1.2%
6.323
 
1.2%
3.663
 
1.2%
5.983
 
1.2%
4.123
 
1.2%
6.263
 
1.2%
5.853
 
1.2%
Other values (175)220
87.0%
ValueCountFrequency (%)
3.311
 
0.4%
3.371
 
0.4%
3.381
 
0.4%
3.41
 
0.4%
3.411
 
0.4%
3.421
 
0.4%
3.432
0.8%
3.483
1.2%
3.521
 
0.4%
3.531
 
0.4%
ValueCountFrequency (%)
8.621
0.4%
8.271
0.4%
8.251
0.4%
8.231
0.4%
8.211
0.4%
8.151
0.4%
8.141
0.4%
8.131
0.4%
7.961
0.4%
7.881
0.4%

gdp_monthly
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct243
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.84720949
Minimum90.834
Maximum101.814
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2021-09-09T18:27:10.439261image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum90.834
5-th percentile97.8752
Q199.614
median100.012
Q3100.535
95-th percentile101.6372
Maximum101.814
Range10.98
Interquartile range (IQR)0.921

Descriptive statistics

Standard deviation1.437940124
Coefficient of variation (CV)0.01440140522
Kurtosis11.83887897
Mean99.84720949
Median Absolute Deviation (MAD)0.449
Skewness-2.706682987
Sum25261.344
Variance2.067671801
MonotonicityNot monotonic
2021-09-09T18:27:10.862355image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99.9953
 
1.2%
100.0152
 
0.8%
99.6512
 
0.8%
99.9272
 
0.8%
99.9552
 
0.8%
99.9572
 
0.8%
99.9592
 
0.8%
99.622
 
0.8%
100.0492
 
0.8%
101.5581
 
0.4%
Other values (233)233
92.1%
ValueCountFrequency (%)
90.8341
0.4%
92.3811
0.4%
92.5041
0.4%
94.1651
0.4%
95.3221
0.4%
95.8111
0.4%
95.9491
0.4%
96.0811
0.4%
96.211
0.4%
96.4511
0.4%
ValueCountFrequency (%)
101.8141
0.4%
101.8031
0.4%
101.7971
0.4%
101.7651
0.4%
101.7641
0.4%
101.7321
0.4%
101.7311
0.4%
101.7231
0.4%
101.7041
0.4%
101.71
0.4%

hcai
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct85
Distinct (%)33.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.829371542
Minimum2.293
Maximum21.774
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2021-09-09T18:27:11.019448image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2.293
5-th percentile2.342
Q12.614
median5.199
Q316.048
95-th percentile21.088
Maximum21.774
Range19.481
Interquartile range (IQR)13.434

Descriptive statistics

Standard deviation7.06195348
Coefficient of variation (CV)0.799825157
Kurtosis-1.354237668
Mean8.829371542
Median Absolute Deviation (MAD)2.767
Skewness0.5977622705
Sum2233.831
Variance49.87118695
MonotonicityNot monotonic
2021-09-09T18:27:11.181650image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.3143
 
1.2%
3.6133
 
1.2%
4.653
 
1.2%
2.7913
 
1.2%
15.7393
 
1.2%
18.7153
 
1.2%
3.2083
 
1.2%
21.7743
 
1.2%
19.953
 
1.2%
2.3423
 
1.2%
Other values (75)223
88.1%
ValueCountFrequency (%)
2.2933
1.2%
2.3143
1.2%
2.3283
1.2%
2.3353
1.2%
2.3423
1.2%
2.3573
1.2%
2.3753
1.2%
2.4093
1.2%
2.4223
1.2%
2.4323
1.2%
ValueCountFrequency (%)
21.7743
1.2%
21.4373
1.2%
21.3623
1.2%
21.3213
1.2%
21.0883
1.2%
20.3273
1.2%
20.1813
1.2%
20.093
1.2%
19.953
1.2%
19.6873
1.2%

employment_percentage
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct251
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.9176166
Minimum60.29
Maximum74.505
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2021-09-09T18:27:11.333214image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum60.29
5-th percentile66.6098
Q167.812
median70.581
Q371.67
95-th percentile73.869
Maximum74.505
Range14.215
Interquartile range (IQR)3.858

Descriptive statistics

Standard deviation2.358978719
Coefficient of variation (CV)0.03373940408
Kurtosis0.1588556446
Mean69.9176166
Median Absolute Deviation (MAD)1.469
Skewness-0.4553236566
Sum17689.157
Variance5.564780594
MonotonicityNot monotonic
2021-09-09T18:27:11.490297image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.6842
 
0.8%
71.4192
 
0.8%
66.5961
 
0.4%
69.921
 
0.4%
71.8841
 
0.4%
71.1981
 
0.4%
67.4291
 
0.4%
72.1731
 
0.4%
66.9591
 
0.4%
71.2581
 
0.4%
Other values (241)241
95.3%
ValueCountFrequency (%)
60.291
0.4%
62.2151
0.4%
64.7531
0.4%
65.3771
0.4%
66.3851
0.4%
66.4161
0.4%
66.4261
0.4%
66.4491
0.4%
66.4591
0.4%
66.511
0.4%
ValueCountFrequency (%)
74.5051
0.4%
74.271
0.4%
74.2481
0.4%
74.2271
0.4%
74.2121
0.4%
74.1031
0.4%
74.0891
0.4%
73.9611
0.4%
73.9261
0.4%
73.9011
0.4%

ppi_const_goods
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct201
Distinct (%)79.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean188.0814229
Minimum140.1
Maximum240.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2021-09-09T18:27:11.646989image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum140.1
5-th percentile141.1
Q1160.7
median192.2
Q3211.6
95-th percentile230.12
Maximum240.6
Range100.5
Interquartile range (IQR)50.9

Descriptive statistics

Standard deviation30.0545247
Coefficient of variation (CV)0.1597952857
Kurtosis-1.233395819
Mean188.0814229
Median Absolute Deviation (MAD)21.2
Skewness-0.3128385544
Sum47584.6
Variance903.2744551
MonotonicityNot monotonic
2021-09-09T18:27:11.804064image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
142.47
 
2.8%
141.34
 
1.6%
2113
 
1.2%
1413
 
1.2%
140.33
 
1.2%
141.13
 
1.2%
184.43
 
1.2%
211.73
 
1.2%
177.52
 
0.8%
211.42
 
0.8%
Other values (191)220
87.0%
ValueCountFrequency (%)
140.11
 
0.4%
140.33
1.2%
140.61
 
0.4%
140.82
0.8%
140.92
0.8%
1413
1.2%
141.13
1.2%
141.22
0.8%
141.34
1.6%
141.42
0.8%
ValueCountFrequency (%)
240.61
0.4%
234.31
0.4%
232.51
0.4%
232.12
0.8%
231.91
0.4%
231.81
0.4%
231.71
0.4%
231.31
0.4%
231.21
0.4%
230.91
0.4%

cci_real_estate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct246
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.83890119
Minimum96.16
Maximum103.151
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2021-09-09T18:27:11.945132image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum96.16
5-th percentile96.8284
Q198.627
median100.176
Q3101.039
95-th percentile101.724
Maximum103.151
Range6.991
Interquartile range (IQR)2.412

Descriptive statistics

Standard deviation1.596925586
Coefficient of variation (CV)0.01599502365
Kurtosis-0.4077544397
Mean99.83890119
Median Absolute Deviation (MAD)1.051
Skewness-0.4169900836
Sum25259.242
Variance2.550171327
MonotonicityNot monotonic
2021-09-09T18:27:12.102223image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98.6212
 
0.8%
101.2272
 
0.8%
101.4542
 
0.8%
100.9422
 
0.8%
101.1752
 
0.8%
100.9242
 
0.8%
100.4352
 
0.8%
98.4731
 
0.4%
100.1711
 
0.4%
97.5121
 
0.4%
Other values (236)236
93.3%
ValueCountFrequency (%)
96.161
0.4%
96.1821
0.4%
96.191
0.4%
96.1911
0.4%
96.2581
0.4%
96.2741
0.4%
96.3351
0.4%
96.3921
0.4%
96.4111
0.4%
96.4851
0.4%
ValueCountFrequency (%)
103.1511
0.4%
103.0911
0.4%
103.0591
0.4%
103.0381
0.4%
103.0091
0.4%
102.8961
0.4%
102.8581
0.4%
102.8181
0.4%
102.7611
0.4%
102.6371
0.4%

deliquency_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct77
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.59229249
Minimum0.68
Maximum8.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2021-09-09T18:27:12.258919image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.68
5-th percentile0.7
Q11.02
median1.51
Q33.28
95-th percentile8.44
Maximum8.92
Range8.24
Interquartile range (IQR)2.26

Descriptive statistics

Standard deviation2.406875791
Coefficient of variation (CV)0.9284738509
Kurtosis0.8090266698
Mean2.59229249
Median Absolute Deviation (MAD)0.58
Skewness1.469235907
Sum655.85
Variance5.793051073
MonotonicityNot monotonic
2021-09-09T18:27:12.462387image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.689
 
3.6%
0.79
 
3.6%
1.116
 
2.4%
0.776
 
2.4%
1.026
 
2.4%
1.636
 
2.4%
8.923
 
1.2%
8.513
 
1.2%
1.053
 
1.2%
1.153
 
1.2%
Other values (67)199
78.7%
ValueCountFrequency (%)
0.689
3.6%
0.79
3.6%
0.723
 
1.2%
0.743
 
1.2%
0.753
 
1.2%
0.776
2.4%
0.783
 
1.2%
0.823
 
1.2%
0.863
 
1.2%
0.873
 
1.2%
ValueCountFrequency (%)
8.923
1.2%
8.763
1.2%
8.583
1.2%
8.513
1.2%
8.443
1.2%
8.033
1.2%
7.853
1.2%
7.513
1.2%
7.093
1.2%
6.73
1.2%

m3_trillion
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct253
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.56049249
Minimum4.6669
Maximum19.3965
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2021-09-09T18:27:12.619472image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum4.6669
5-th percentile4.99878
Q16.4557
median8.6194
Q312.1965
95-th percentile15.36146
Maximum19.3965
Range14.7296
Interquartile range (IQR)5.7408

Descriptive statistics

Standard deviation3.594104146
Coefficient of variation (CV)0.37593295
Kurtosis-0.4043390599
Mean9.56049249
Median Absolute Deviation (MAD)2.5558
Skewness0.6419186861
Sum2418.8046
Variance12.91758461
MonotonicityNot monotonic
2021-09-09T18:27:12.776159image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.50481
 
0.4%
13.97511
 
0.4%
13.29531
 
0.4%
5.34821
 
0.4%
7.27751
 
0.4%
8.47151
 
0.4%
5.33681
 
0.4%
9.79351
 
0.4%
9.93871
 
0.4%
19.39651
 
0.4%
Other values (243)243
96.0%
ValueCountFrequency (%)
4.66691
0.4%
4.68011
0.4%
4.71081
0.4%
4.75461
0.4%
4.76681
0.4%
4.77241
0.4%
4.791
0.4%
4.81811
0.4%
4.85381
0.4%
4.86971
0.4%
ValueCountFrequency (%)
19.39651
0.4%
19.13141
0.4%
18.96021
0.4%
18.75111
0.4%
18.6051
0.4%
18.38181
0.4%
18.321
0.4%
18.17961
0.4%
17.8931
0.4%
17.04291
0.4%

Interactions

2021-09-09T18:26:46.850030image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:47.288760image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:47.414603image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:47.524403image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:47.649855image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:47.775286image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:47.900722image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:48.026547image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:48.151980image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:48.277409image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:48.402828image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:48.513633image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:48.638450image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:48.763885image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:48.873691image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:48.983506image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:49.108430image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:49.234267image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:49.438233image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:49.563658image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:49.673467image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:49.798908image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:49.908719image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:50.018922image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:50.128725image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:50.269790image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:50.379586image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:50.489390image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:50.599186image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:50.693360image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:50.803170image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:50.928487image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:51.038299image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:51.157377image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:51.282807image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:51.376975image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:51.486771image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:51.620218image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:51.738043image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:51.863482image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:51.988912image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:52.098715image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:52.240174image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:52.365598image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:52.492227image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:52.743500image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:52.884557image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:52.994358image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:53.167081image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:53.276886image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:53.401809image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:53.511616image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:53.624424image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:53.731615image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:53.825788image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:53.951233image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:54.061039image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:54.170843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:54.280639image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:54.390442image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:54.500237image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:54.625666image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:54.735854image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:54.861281image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:54.971080image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:55.080891image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:55.227953image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:55.374685image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:55.484489image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:55.594286image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:55.704087image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:55.813890image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:55.939718image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:56.080792image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:56.221711image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:56.342500image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:56.483575image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:56.592876image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:56.875427image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:57.016483image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:57.142299image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:57.298985image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:57.432432image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:57.534613image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:57.675673image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:57.801100image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:57.926532image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:58.036724image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:58.177784image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:58.308970image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:58.434398image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:58.560231image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:58.701292image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:58.889154image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:59.014588image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:59.143024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:59.312725image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:59.454175image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:59.563989image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:59.673796image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:59.799230image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:26:59.893415image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:00.018845image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:00.145292image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:00.254474image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:00.364281image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:00.489715image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:00.599524image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:00.724448image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:00.881530image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:01.006967image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:01.116769image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:01.258224image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:01.383659image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:01.509094image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:01.666174image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:01.791613image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:01.949332image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:02.309395image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:02.433771image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:02.559592image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:02.685034image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:02.779199image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:02.889006image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:02.998810image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:03.092980image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:03.218405image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:03.328202image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:03.438009image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:03.547814image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:03.657498image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:03.757792image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:03.861482image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:03.986923image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:04.112353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:04.237785image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:04.363611image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:04.489048image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:04.598851image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:04.739907image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:04.860238image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:04.975563image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:05.101007image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-09T18:27:05.226436image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-09-09T18:27:12.917613image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-09T18:27:13.529612image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-09T18:27:13.764839image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-09T18:27:13.987954image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-09-09T18:27:05.963187image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-09T18:27:06.363724image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

target_home_pricepopulationmonthly_supply_housesnew_permits_thousandsmortgage_rategdp_monthlyhcaiemployment_percentageppi_const_goodscci_real_estatedeliquency_ratem3_trillion
0100.5522810834.317278.15101.48817.08774.248142.4103.0911.484.6669
1101.3392812994.316928.25101.54717.08774.270142.7103.1511.484.6801
2102.1272815314.316518.27101.62017.08774.227143.2103.0591.484.7108
3102.9222817634.415978.23101.69016.59474.505143.2103.0381.444.7668
4103.6772819964.415438.13101.73116.59474.089142.2103.0091.444.7546
5104.4242822474.815728.62101.72316.59474.212142.4102.8961.444.7724
6105.0542825044.115428.14101.67015.35773.837141.9102.8581.474.7900
7105.7682827694.415528.21101.58315.35773.885141.2102.8181.474.8181
8106.5372830334.015707.96101.47215.35773.872141.3102.7611.474.8538
9107.3812832854.015777.88101.34015.77973.891141.1102.6371.524.8697

Last rows

target_home_pricepopulationmonthly_supply_housesnew_permits_thousandsmortgage_rategdp_monthlyhcaiemployment_percentageppi_const_goodscci_real_estatedeliquency_ratem3_trillion
243217.5543297606.610944.5192.3812.85160.290215.298.8870.9317.0429
244217.8103298945.312464.6590.8342.85162.215216.598.3250.9317.8930
245218.3563300384.312964.8592.5042.85164.753221.298.2690.9318.1796
246219.8993301993.615424.9494.1652.68865.377225.398.2891.0018.3200
247222.7353303683.515224.6395.8112.68866.831228.998.5161.0018.3818
248225.9543305353.515894.4595.9492.68866.959231.998.8671.0018.6050
249229.4423306923.515954.4196.0812.74467.837231.899.0341.1318.7511
250232.6563308294.016964.4196.2102.74467.879230.798.9861.1318.9602
251235.5833309243.817584.0896.4512.74468.025234.398.9891.1319.1314
252238.5303309683.618834.1496.6922.80068.202240.698.9941.0119.3965